Besides monocular depth estimation, Multi-View Stereo (MVS) is another group of methods for predicting depth. Given a set of images with known camera poses, MVS approaches [1]}, [2]} can produce highly accurate depth estimates with multi-view geometric constraints.
One example is MVSNet [2]}, in which the variance-based cost volume is presented to fuse multiple features maps from source images into one unified cost volume. Stereo matching can be treated as a special case of MVS.
Conventionally, stereo-based depth estimation methods [4]}, [5]} relied on matching pixels across stereo images. Many recent stereo matching approaches [6]}, [7]} leveraged CNNs for feature extraction, cost matching, and aggregation. For example, PSMNet [7]} incorporated spatial pyramid pooling (SPP) module and multi-scale 3D hourglass modules to further boost the performance. To improve the efficiency and accelerate training on high-resolution images, Gu et al. [9]} presented a cascade cost volume design to gradually retrieve finer hypothesis plane ranging over multiple steps.
